-
摘要: 步态识别作为生物特征识别中的一种，具有远距离、非接触和难以模仿等优点.其中视角或行走方向的变化使提取的人体轮廓产生巨大差异，是影响步态识别系统性能的最主要因素之一.本文首先介绍了现有的多角度步态数据库，然后根据特征提取方式的不同，将当前已提出的方法分为三维模型法、视角不变性特征法、映射投影法和深度神经网络法四类，并详细阐述了每一类的原理、特点以及优缺点.最后，结合实际应用指出当前研究的局限性与发展趋势.Abstract: As one of biometric identification methods, gait recognition has many advantages. It is suitable for human identification at a long distance, requiring no contact and hard to imitate. One of the most important factors that affect the performance of gait recognition is that the change of view or the direction of the walk, which makes the human silhouette have a large variation. We first introduce the existing multi-view gait database. Then according to the characteristics of different extraction methods, the current approaches are divided into four kinds:three-dimensional model, view-invariant features, subspace projection as well as deep neural network, and analyses are conducted on their principle, advantages and disadvantages. Finally the limitations of current research and development trend are pointed out.
-
Key words:
- Gait recognition /
- multi-view /
- behavioral characteristics recognition /
- biometrics
1) 本文责任编委 王亮 -
表 1 多视角步态库
Table 1 Databases for multiview gait
库名 建立机构 样本容量 具体角度 示例 USF[3] 南佛罗里达大学 122人, 1 870序列(地面、鞋子、负重和时间) 在摄像机前(左右两个)绕椭圆路线行走 
CASIA-A[13] 中国科学院自动化研究所 20人×3视角× 4序列= 240 3个(侧面的3个点) 
CASIA-B[14] 中国科学院自动化研究所 124人× 11视角× (8正常+2背包+ 2外套) = 13 640 11个($180^{\circ}$每间隔$18^{\circ}$度一个视角) 
HID-UMD[15] 马里兰大学 1: 25人× 4个视角= 100 正面(走向、走出)、侧面(向左、向右) 
2: 55人× 4 (2视角) = 220 T形路径(正面、侧面) 
CMU MoBo[16] 卡耐基梅隆大学 25人× 6视角× (3速度+1抱球+ 1上坡) = 600 6个($360^{\circ}$圆周每$60^{\circ}$一个视角) 
OU-ISIR Treadmill[17] 大阪大学 168人 25个(12个方位角× 2个倾斜角度+1个俯视) 
OU-ISIR LP[18] 大阪大学 4 007人 8个(2摄像机$\times $ 4个侧面角度) 
SZU RBG-D[19] 深圳大学 99人× 2视角× 4序列= 792 $90^{\circ}$和$30^{\circ} \sim 60^{\circ}$之间的一个角度 
下载: 导出CSV
表 2 类能量图构造方法与性能分析
Table 2 The construction methods and performances analysis of class energy image
表 3 CASIA-B数据集上现有步态识别方法的准确率对比
Table 3 Recognition accuracy of existing approaches on CASIA-B datasets
方法 识别率(%) $54^{\circ}$ $72^{\circ}$ $90^{\circ}$ $108^{\circ}$ $126^{\circ}$ 3D模型局部能量图投影[31] 86.1 98.1 - 92.3 90.4 下肢姿态重构[47] 74.4 72.6 86.5 69.2 67.4 聚类AGI +SRML[48] 68.3 92.7 - 93.4 70.3 聚类EDI +IT2FKNN[49] 72.2 94.8 - 95.3 74.9 GEI+CCA[53] 52.0 94.6 - 93.8 78.3 C3A[55] 86.6 98.7 - 95.4 86.0 GEI+TSVD[59] 53.7 81.8 - 86.3 45.7 低秩GEI+SVD[61] 42.5 86.2 - 88.3 49.8 JSL[68] 71.4 91.7 - 90.5 73.6 KCDML[75] 76.7 94.3 - 88.1 88.4 GEI+SPAE[88] 82.3 94.4 96.0 96.0 86.3
下载: 导出CSV
表 4 现有多视角步态识别方法
Table 4 Existing approaches for multiview gait recognition
模板 方法 实现途径 优点 缺点 三维模板 建立三维步态模型 利用多摄像机对人体结构或人体运动进行3D建模[22, 27-35] 更准确地表达人体各个部位的物理空间; 能够降低遮挡等因素的负面影响. 需要全可控的多摄像机协作的识别环境; 摄像机平衡视角和建模计算复杂. 提取视角不变性特征 提取局部特征[44-47]; 聚类图像估计视角[48, 49]; 其他[38, 51]. 直接提取不随着角度而改变的步态特征进行身份识别, 避免轮廓差异; 思路直观, 计算简单, 易于理解和实现. 仅仅适用于视角变化有限的情况下; 易受到遮挡因素或服饰变化的破坏. 二维图像或视频序列特征 学习不同视角下的映射或投影关系 典范相关分析(CCA)[53-57]; 视角转换模型(VTM)[58-65]; 其他(LDA、CML、MPCA与核扩展等)[66-77]. 投影到子空间中获得步态的视角不变特征, 减小同一行人不同视角下的类内方差; 具有相对较高的识别精度. 步态图像转换成向量后维数常高达上万维, 计算量很大; 视角变化较大时效果不理想; CCA和CML类的方法仅能利用两个视角间的互补信息, 处理N个视角时要重复N次来学习N对特征映射; VTM方法在进行模型构建和视角转化时容易造成噪声传播, 致使识别性能退化. 基于深度神经网络 CNN[80-86]与AutoEncoder[88] 无图像的预处理过程; 有效提取步态特征; 具有相对较高的识别精度. 需要大量数据用于训练; 卷积神经网络缺乏对时间序列信号的记忆功能; 基于自动编码的VTM也有在转化时容易造成噪声传播使识别性能退化的问题.
下载: 导出CSV
-
[1] Phillips P J. Human identification technical challenges. In: Proceedings of the 2002 International Conference on Image Processing. New York, USA: IEEE, 2002. I-49-I-52 [2] Tariq M, Shah M A. Review of model-free gait recognition in biometrie systems. In: Proceedings of the 23rd International Conference on Automation and Computing. Huddersfield, UK: IEEE, 2017. 1-7 [3] Sarkar S, Phillips P J, Liu Z, Vega, I R, Grother P, Bowyer K W. The humanID gait challenge problem:data sets, performance, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2):162-177 doi: 10.1109/TPAMI.2005.39 [4] Liu Z, Sarkar S. Effect of silhouette quality on hard problems in gait recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005, 35(2):170-183 doi: 10.1109/TSMCB.2004.842251 [5] Kale A, Sundaresan A, Rajagopalan A N, Cuntoor N P, Roy-Chowdhury A K, Kruger V, et al. Identification of humans using gait. IEEE Transactions on Image Processing, 2004, 13(9):1163-1173 doi: 10.1109/TIP.2004.832865 [6] Masood H, Farooq H. A proposed framework for vision based gait biometric system against spoofing attacks. In: Proceedings of the 2017 International Conference on Communication, Computing and Digital Systems. Islamabad, Pakistan: IEEE, 2017. 357-362 [7] Arseneau S, Cooperstock J R. Real-time image segmentation for action recognition. In: Proceedings of the 1999 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. British Columbia, Canada: IEEE, 1999. 86-89 [8] Verlekar T T, Correia P L, Soares L D. View-invariant gait recognition system using a gait energy image decomposition method. IET Biometrics, 2017, 6(4):299-306 doi: 10.1049/iet-bmt.2016.0118 [9] Niyogi S A, Adelson E H. Analyzing gait with spatiotemporal surfaces. In: Proceedings of the 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects. Texas, USA: IEEE, 1994. 64-69 [10] 王科俊, 侯本博.步态识别综述.中国图象图形学报, 2007, 12(7):1152-1160 doi: 10.3969/j.issn.1006-8961.2007.07.002Wang Ke-Jun, Hou Ben-Bo. A survey of gait recognition. Journal of Image and Graphics, 2007, 12(7):1152-1160 doi: 10.3969/j.issn.1006-8961.2007.07.002 [11] Sugandhi K, Wahid F F, Raju G. Feature extraction methods for human gait recognition——a survey. In: Proceedings of the 2017 Advances in Computing and Data Sciences. Communications in Computer and Information Science, vol. 721. Singapore: Springer, 2017. 377-385 [12] Lv Z W, Xing X L, Wang K J, Guan D H. Class energy image analysis for video sensor-based gait recognition:a review. Sensors, 2015, 15(1):932-964 [13] Wang L, Tan T N, Ning H Z, Hu W M. Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12):1505-1518 doi: 10.1109/TPAMI.2003.1251144 [14] Yu S Q, Tan D L, Tan T N. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proceedings of 18th International Conference on Pattern Recognition. Hong Kong, China: IEEE, 2006. 441-444 [15] Chalidabhongse T, Kruger V, Chellappa R. The UMD Database for Human Identification at a Distance, Technical Report, University of Maryland, USA, 2001 [16] Gross R, Shi J B. The CMU Motion of Body (MoBo) Database, Technical Report CMU-RI-TR-01-18, Carnegie Mellon University, USA, 2001 [17] Makihara Y, Mannami H, Yagi Y. Gait analysis of gender and age using a large-scale multi-view gait database computer vision. In: Proceedings of Asian Conference on Computer Vision. Queenstown, New Zealand: ACM, 2010. 440-451 [18] Iwama H, Okumura M, Makihara Y, Yagi Y. The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Transactions on Information Forensics and Security, 2012, 7(5):1511-1521 doi: 10.1109/TIFS.2012.2204253 [19] Yu S Q, Wang Q, Huang Y Z. A large RGB-D gait dataset and the baseline algorithm. In: Biometric Recognition. Lecture Notes in Computer Science, vol. 8232. Cham: Springer, 2013. 417-424 [20] Seely R D, Samangooei S, Lee M, Carter J N, Nixon M S. The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems. Virginia USA: IEEE, 2008. 1-6 [21] Anguelov D, Koller D, Pang H C, Srinivasan P, Thrun S. Recovering articulated object models from 3D range data. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. Banff, Canada: ACM, 2004. 18-26 [22] López-Fernández D, Madrid-Cuevas F J, Carmona-Poyato A, Muñoz-Salinas R, Medina-Carnicer R. A new approach for multi-view gait recognition on unconstrained paths. Journal of Visual Communication and Image Representation, 2016, 38:396-406 doi: 10.1016/j.jvcir.2016.03.020 [23] Hofmann M, Sural S, Rigoll G. Gait recognition in the presence of occlusion: a new dataset and baseline algorithms. In: Proceedings of the 19th International Conference in Central Europe Computer Graphics, Visualization and Computer Vision. Plzen, Czech Republic: Václav Skala UNION Agency, 2011. 99-104 [24] Cho C W, Chao W H, Lin S H, Chen Y Y. A vision-based analysis system for gait recognition in patients with Parkinson's disease. Expert Systems with Applications, 2009, 36(3):7033-7039 doi: 10.1016/j.eswa.2008.08.076 [25] Stevenage S V, Nixon M S, Vince K. Visual analysis of gait as a cue to identity. Applied Cognitive Psychology, 1999, 13(6):513-526 doi: 10.1002/(ISSN)1099-0720 [26] Lai D T H, Begg R K, Palaniswami M. Computational intelligence in gait research:a perspective on current applications and future challenges. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(5):687-702 doi: 10.1109/TITB.2009.2022913 [27] Shakhnarovich G, Lee L, Darrell T. Integrated face and gait recognition from multiple views. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Hawaii, USA: IEEE, 2001. 439-446 [28] Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N. View-independent human motion classification using image-based reconstruction. Image and Vision Computing, 2009, 27(8):1194-1206 doi: 10.1016/j.imavis.2008.11.008 [29] Zhang Z H, Troje N F. View-independent person identification from human gait. Neurocomputing, 2005, 69(1-3):250-256 doi: 10.1016/j.neucom.2005.06.002 [30] Tang J, Luo J, Tjahjadi T, Gao Y. 2.5D multi-view gait recognition based on point cloud registration. Sensors, 2014, 14(4):6124-6143 doi: 10.3390/s140406124 [31] Tang J, Luo J, Tjahjadi T, Guo F. Robust arbitrary-view gait recognition based on 3D partial similarity matching. IEEE Transactions on Image Processing, 2017, 26(1):7-22 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=db1c9453b08e2468c550f2b43633a9b4 [32] Zhao G Y, Liu G Y, Li H, Pietikinen M. 3D gait recognition using multiple cameras. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. Southampton, UK: IEEE, 2006. 529-534 [33] Deng M Q, Wang C, Chen Q F. Human gait recognition based on deterministic learning through multiple views fusion. Pattern Recognition Letters, 2016, 78:56-63 doi: 10.1016/j.patrec.2016.04.004 [34] Deng M Q, Wang C, Cheng F J, Zeng W. Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning. Pattern Recognition, 2017, 67:186-200 doi: 10.1016/j.patcog.2017.02.014 [35] Iwashita Y, Ogawara K, Kurazume R. Identification of people walking along curved trajectories. Pattern Recognition Letters, 2014, 48:60-69 doi: 10.1016/j.patrec.2014.04.004 [36] Bobick A F, Davis J W. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(3):257-267 doi: 10.1109/34.910878 [37] Han J, Bhanu B. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(2):316-323 doi: 10.1109/TPAMI.2006.38 [38] Lam T H W, Lee R S T. A new representation for human gait recognition: motion silhouettes image (MSI). In: Advances in Biometrics. Lecture Notes in Computer Science, vol. 3832. Berlin, Heidelberg: Springer-Verlag, 2005. 612-618 [39] Liu J Y, Zheng N N. Gait history image: a novel temporal template for gait recognition. In: Proceedings of the 2007 IEEE International Conference on Multimedia and Expo. Beijing, China: IEEE, 2007. 663-666 [40] Zhang E H, Zhao Y W, Xiong W. Active energy image plus 2DLPP for gait recognition. Signal Processing, 2010, 90(7):2295-2302 doi: 10.1016/j.sigpro.2010.01.024 [41] Bashir K, Xiang T, Gong S G. Gait recognition without subject cooperation. Pattern Recognition Letters, 2010, 31(13):2052-2060 doi: 10.1016/j.patrec.2010.05.027 [42] Chen C H, Liang J M, Zhu X C. Gait recognition based on improved dynamic Bayesian networks. Pattern Recognition, 2011, 44(4):988-995 doi: 10.1016/j.patcog.2010.10.021 [43] Wang C, Zhang J P, Wang L, Pu J, Yuan X R. Human identification using temporal information preserving gait template. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2164-2176 doi: 10.1109/TPAMI.2011.260 [44] Jean F, Albu A B, Bergevin R. Towards view-invariant gait modeling:computing view-normalized body part trajectories. Pattern Recognition, 2009, 42(11):2936-2949 doi: 10.1016/j.patcog.2009.05.006 [45] Ng H, Tan W H, Abdullah J, Tong H L. Development of vision based multiview gait recognition system with MMUGait database. The Scientific World Journal, 2014, 2014:Article ID 376569 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000003705025 [46] 彭彰, 吴晓娟, 杨军.基于肢体长度参数的多视角步态识别算法.自动化学报, 2007, 33(2):210-213 http://www.aas.net.cn/CN/abstract/abstract13832.shtmlPeng Zhang, Wu Xiao-Juan, Yang Jun. A multi-view method for gait recognition based on the length of body's parts. Acta Automatica Sinica, 2007, 33(2):210-213 http://www.aas.net.cn/CN/abstract/abstract13832.shtml [47] Goffredo M, Bouchrika I, Carter J N, Nixon M S. Self-calibrating view-invariant gait biometrics. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 40(4):997-1008 doi: 10.1109/TSMCB.2009.2031091 [48] Lu J W, Wang G, Moulin P. Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Transactions on Information Forensics and Security, 2014, 9(1):51-61 doi: 10.1109/TIFS.2013.2291969 [49] Darwish S M. Design of adaptive biometric gait recognition algorithm with free walking directions. IET Biometrics, 2017, 6(2):53-60 doi: 10.1049/iet-bmt.2015.0082 [50] Ma Q Y, Wang S K, Nie D D, Qiu J F. Gait recognition at a distance based on energy deviation image. In: Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering. Wuhan, China: IEEE, 2007. 621-624 [51] Kale A, Chowdhury A K R, Chellappa R. Towards a view invariant gait recognition algorithm. In: Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance. Miami, Florida, USA: IEEE, 2003. 143-150 [52] Hotelling H. Relations between two sets of variates. Biometrika, 1936, 28(3-4):321-377 doi: 10.1093/biomet/28.3-4.321 [53] Bashir K, Xiang T, Gong S G. Cross-view gait recognition using correlation strength. In: Proceedings of the British Machine Vision Conference. Aberystwyth, UK: BMVA Press, 2010. 109.1-109.11 [54] Hu H F. Multiview gait recognition based on patch distribution features and uncorrelated multilinear sparse local discriminant canonical correlation analysis. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(4):617-630 doi: 10.1109/TCSVT.2013.2280098 [55] Xing X L, Wang K J, Yan T, Lv Z W. Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognition, 2016, 50:107-117 doi: 10.1016/j.patcog.2015.08.011 [56] Wang K J, Yan T. An improved kernelized discriminative canonical correlation analysis and its application to gait recognition. In: Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China: IEEE, 2012. 4869-4874 [57] Luo C, Xu W J, Zhu C Y. Robust gait recognition based on partitioning and canonical correlation analysis. In: Proceedings of the 2015 IEEE International Conference on Imaging Systems and Techniques. Macau, China: IEEE, 2015. 1-5 [58] Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y. Gait recognition using a view transformation model in the frequency domain. In: Proceedings of European Conference on Computer Vision. Graz, Austria: Springer-Verlag, 2006. 151-163 [59] Kusakunniran W, Wu Q, Li H D, Zhang J. Multiple views gait recognition using view transformation model based on optimized gait energy image. In: Proceedings of the 12th International Conference on Computer Vision Workshops. Kyoto, Japan: IEEE, 2009. 1058-1064 [60] Kusakunniran W, Wu Q, Zhang J, Li H D. Gait recognition under various viewing angles based on correlated motion regression. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(6):966-980 doi: 10.1109/TCSVT.2012.2186744 [61] Zheng S, Zhang J G, Huang K Q, He R, Tan T. Robust view transformation model for gait recognition. In: Proceedings of the 18th International Conference on Image Processing. Brussels, Belgium: IEEE, 2011. 2073-2076 [62] Hu M D, Wang Y H, Zhang Z X. Cross-view gait recognition with short probe sequences:from view transformation model to view-independent stance-independent identity vector. International Journal of Pattern Recognition and Artificial Intelligence, 2013, 27(6):1350017 doi: 10.1142/S0218001413500171 [63] Muramatsu D, Shiraishi A, Makihara Y, Uddin M Z, Yagi Y. Gait-based person recognition using arbitrary view transformation model. IEEE Transactions on Image Processing, 2015, 24(1):140-54 doi: 10.1109/TIP.2014.2371335 [64] Muramatsu D, Makihara Y, Yagi Y. Cross-view gait recognition by fusion of multiple transformation consistency measures. IET Biometrics, 2015, 4(2):62-73 doi: 10.1049/iet-bmt.2014.0042 [65] Muramatsu D, Makihara Y, Yagi Y. View transformation model incorporating quality measures for cross-view gait recognition. IEEE Transactions on Cybernetics, 2016, 46(7):1602-1615 doi: 10.1109/TCYB.2015.2452577 [66] Choudhury S D, Tjahjadi T. Robust view-invariant multiscale gait recognition. Pattern Recognition, 2015, 48(3):798-811 doi: 10.1016/j.patcog.2014.09.022 [67] Liu N, Tan Y P. View invariant gait recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Texas USA: IEEE, 2010. 1410-1413 [68] Liu N N, Lu J W, Tan Y P. Joint subspace learning for view-invariant gait recognition. IEEE Signal Processing Letters, 2011, 18(7):431-434 doi: 10.1109/LSP.2011.2157143 [69] Shawe-Taylor J, Cristianini N. Properties of kernels. Kernel Methods for Pattern Analysis. Cambridge:Cambridge University Press, 2004. 47-84 [70] Alzate C, Suykens J A K. Multiway spectral clustering with out-of-sample extensions through weighted kernel PCA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(2):335-347 doi: 10.1109/TPAMI.2008.292 [71] Yang J, Frangi A F, Yang J Y, Zhang D, Jin Z. KPCA Plus LDA:a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2):230-244 doi: 10.1109/TPAMI.2005.33 [72] Connie T, Goh K O M, Teoh A B J. Multi-view gait recognition using a doubly-kernel approach on the Grassmann manifold. Neurocomputing, 2016, 216:534-542 doi: 10.1016/j.neucom.2016.08.002 [73] Xu W J, Luo C, Ji A M, Zhu C Y. Coupled locality preserving projections for cross-view gait recognition. Neurocomputing, 2017, 224:37-44 doi: 10.1016/j.neucom.2016.10.054 [74] Ben X Y, Meng W X, Yan R, Wang K J. An improved biometrics technique based on metric learning approach. Neurocomputing, 2012, 97(1):44-51 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4492014b0866d51f7d2415c3699c60d8 [75] Ben X Y, Meng W X, Yan R, Wang K J. Kernel coupled distance metric learning for gait recognition and face recognition. Neurocomputing, 2013, 120:577-589 doi: 10.1016/j.neucom.2013.04.012 [76] Wang K J, Xing X L, Yan T, Lv Z W. Couple metric learning based on separable criteria with its application in cross-view gait recognition. In: Proceedings of the Biometric Recognition. Lecture Notes in Computer Science, vol. 8833. Shenyang, China: Springer, 2014. 347-356 [77] Al-Tayyan A, Assaleh K, Shanableh T. Decision-level fusion for single-view gait recognition with various carrying and clothing conditions. Image and Vision Computing, 2017, 61:54-69 doi: 10.1016/j.imavis.2017.02.004 [78] 陈伟宏, 安吉尧, 李仁发, 李万里.深度学习认知计算综述.自动化学报, 2017, 43(11):1886-1897 http://www.aas.net.cn/CN/abstract/abstract19164.shtmlChen Wei-Hong, An Ji-Yao, Li Ren-Fa, Li Wan-Li. Review on deep-learning-based cognitive computing. Acta Automatica Sinica, 2017, 43(11):1886-1897 http://www.aas.net.cn/CN/abstract/abstract19164.shtml [79] LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. In: Proceedings of the 2010 IEEE International Symposium on Circuits and Systems. Paris, France: IEEE, 2010. 253-256 [80] Yan C, Zhang B L, Coenen F. Multi-attributes gait identification by convolutional neural networks. In: Proceedings of the 8th International Congress on Image and Signal Processing. Shenyang, China: IEEE, 2015. 642-647 [81] Wu Z F, Huang Y Z, Wang L. Learning representative deep features for image set analysis. IEEE Transactions on Multimedia, 2015, 17(11):1960-1968 doi: 10.1109/TMM.2015.2477681 [82] Zhang C, Liu W, Ma H D, Fu H Y. Siamese neural network based gait recognition for human identification. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China: IEEE, 2016. 2832-2836 [83] Tan T N, Wang L, Huang Y Z, Wu Z F. A Gait Recognition Method Based on Depth Learning, CN Patent 201410587758, June 2017 [84] Wu Z F, Huang Y Z, Wang L, Wang X G, Tan T N. A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2):209-226 doi: 10.1109/TPAMI.2016.2545669 [85] Wolf T, Babaee M, Rigoll G. Multi-view gait recognition using 3D convolutional neural networks. In: Proceedings of the 2016 IEEE International Conference on Image Processing. Arizona USA: IEEE, 2016. 4165-4169 [86] Li C, Min X, Sun S Q, Lin W Q, Tang Z C. DeepGait:a learning deep convolutional representation for view-invariant gait recognition using joint Bayesian. Applied Sciences, 2017, 7(3):210 doi: 10.3390/app7030210 [87] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 2015 International Conference on Pattern Recognition. California, USA: ICPR, 2015. 212-218 [88] Yu S Q, Chen H F, Wang Q, Shen L L, Huang Y Z. Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing, 2017, 239:81-93 doi: 10.1016/j.neucom.2017.02.006 [89] Gers F A, Schraudolph N N, Schmidhuber J. Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 2002, 3:115-143 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=89ea080d0ed753eb233a54138e933da4 [90] 王坤峰, 苟超, 段艳杰, 林懿伦, 郑心湖, 王飞跃.生成式对抗网络GAN的研究进展与展望.自动化学报, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtmlWang Kun-Feng, Gou Chao, Duan Yan-Jie, Lin Yi-Lun, Zheng Xin-Hu, Wang Fei-Yue. Generative adversarial networks:The state of the art and beyond. Acta Automatica Sinica, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml [91] Cao Z, Simon T, Wei S E, Sheikh Y. Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Hawaii, USA: CCPR, 2017. 1302-1310 -
图(1) / 表(4)
计量
- 文章访问数: 2986
- HTML全文浏览量: 891
- PDF下载量: 1294
- 被引次数: 0
